The simplest method for gradient estimation may be finite difference approximations. It is easy to understand and implement. However, it produces biased estimators and the computation time is prohibitively long, because at least \(n+1\)  simulation runs are necessary to compute just one observation of the gradient. Single-run unbiased gradient estimation methods have been proposed in the simulation literature, including the IPA method and the likelihood ratio method. Between these two methods, it is reported that when both are applicable, the IPA method typically produces gradient estimators that have smaller variances than the likelihood ratio method does [1]}.
